What is R Markdown? from RStudio, Inc. on Vimeo.
https://bookdown.org/yihui/rmarkdown/document-templates.html
inside R
markdown::markdownToHTML('markdown_example.md',
'markdown_example.html')
command line
R -e "markdown::markdownToHTML('markdown_example.md',
'markdown_example.html')"
command line
export PATH=$PATH:/Applications/RStudio.app/Contents/MacOS/pandoc
R -e "rmarkdown::render('markdown_example.md')"
chunk içinde R kodlarını çalıştırma{r, results='asis'}
iris %>%
tibble::as_tibble() %>%
details::details(summary = 'tibble')
R kodlarını çalıştırma{r global_options, include=FALSE}
knitr::opts_chunk$set(fig.width = 12,
fig.height = 8,
fig.path = 'Figs/',
echo = FALSE,
warning = FALSE,
message = FALSE,
error = FALSE,
eval = TRUE,
tidy = TRUE,
comment = NA)
{r}
data("cancer")
cancer
foreign::write.foreign(df = cancer,
datafile = "data/cancer.sav",
codefile = "data/cancer.spo",
package = "SPSS"
)
{r}
suppressPackageStartupMessages(library("tidyverse"))
suppressPackageStartupMessages(library("survival"))
{tidyverse} {tidylog}
{lubridate} {janitor}
{readxl} {foreign}
{summarytools} {ggstatsplot} {tangram} {finalfit} {psycho} {jmv}
{survival} {survminer}
{report} {kableExtra}
{r}
View(mydata)
glimpse(mydata)
{r}
mydata <- janitor::clean_names(mydata)
{r}
mydata$sontarih <- janitor::excel_numeric_to_date(
as.numeric(mydata$olum_tarihi)
)
{r}
mydata$Outcome <- "Dead"
mydata$Outcome[mydata$olum_tarihi == "yok"] <- "Alive"
{r}
## Recoding mydata$cinsiyet into mydata$Cinsiyet
mydata$Cinsiyet <- recode(mydata$cinsiyet,
"K" = "Kadin",
"E" = "Erkek")
mydata$Cinsiyet <- factor(mydata$Cinsiyet)
{r recode TNM stage}
#pT2N0Mx -> 2
mydata$Tstage <- stringr::str_match(
mydata$patolojik_evre,
paste('(.+)', "N", sep=''))[,2]
)
{r recode TNM2}
mydata <- mydata %>%
mutate(
T_stage = case_when(
grepl(pattern = "T1", x = .$Tstage) == TRUE ~ "T1",
grepl(pattern = "T2", x = .$Tstage) == TRUE ~ "T2",
grepl(pattern = "T3", x = .$Tstage) == TRUE ~ "T3",
grepl(pattern = "T4", x = .$Tstage) == TRUE ~ "T4",
TRUE ~ "Tx"
)
)
{r}
mydata <- mydata %>%
mutate(
TumorPDL1gr1 = case_when(
t_pdl1 < 1 ~ "kucuk1",
t_pdl1 >= 1 ~ "buyukesit1"
)
)
{r}
library(summarytools)
view(dfSummary(colon_s))
A beginner kit for #rstats The Landscape of R Packages for Automated Exploratory Data Analysis https://journal.r-project.org/archive/2019/RJ-2019-033/
@article{RJ-2019-033, author = {Mateusz Staniak and Przemysław Biecek}, title = {{The Landscape of R Packages for Automated Exploratory Data Analysis}}, year = {2019}, journal = {{The R Journal}}, doi = {10.32614/RJ-2019-033}, url = {https://journal.r-project.org/archive/2019/RJ-2019-033/index.html} }
{r, results='asis'}
# cat(names(mydata), sep = " + \n")
library(arsenal)
tab1 <- tableby(~ Cinsiyet +
Yas +
TumorYerlesimi
,
data = mydata)
summary(tab1)
tangram: The Grammar of Tables
Easily generate information-rich, publication-quality tables from R
{r}
mydata %>%
janitor::tabyl(Categorical) %>%
adorn_pct_formatting(rounding = 'half up',
digits = 1) %>%
knitr::kable()
{r crosstable}
mydata %>%
summary_factorlist(dependent = dependent,
explanatory = explanatory,
total_col = TRUE,
p = TRUE,
add_dependent_label = TRUE) -> table
knitr::kable(table, row.names = FALSE, align = c('l', 'l', 'r', 'r', 'r'))
{r ggstatplot, layout='l-page'}
mydata %>%
ggstatsplot::ggbarstats(data = .,
main = Categorical_variable,
condition = dependent_variable
)
{r}
mydata %>%
jmv::descriptives(
data = .,
vars = c(yas),
hist = TRUE,
dens = TRUE,
box = TRUE,
violin = TRUE,
dot = TRUE,
mode = TRUE,
sd = TRUE,
variance = TRUE,
skew = TRUE,
kurt = TRUE,
quart = TRUE)
{r crosstable}
library(finalfit)
mydata %>%
summary_factorlist(dependent = dependent,
explanatory = explanatory,
column = TRUE,
total_col = TRUE,
p = TRUE,
add_dependent_label = TRUE,
na_include=FALSE
# catTest = catTestfisher
) -> table
knitr::kable(table,
row.names = FALSE,
align = c('l', 'l', 'r', 'r', 'r'))
{r define survival time}
mydata$int <- lubridate::interval(
lubridate::ymd(mydata$CerrahiTarih),
lubridate::ymd(mydata$SonTarih)
)
mydata$OverallTime <- lubridate::time_length(mydata$int, "month")
mydata$OverallTime <- round(mydata$OverallTime, digits = 1)
{r}
## Recoding mydata$Outcome into mydata$Outcome2
mydata$Outcome2 <- recode(mydata$Outcome,
"Alive" = "0",
"Dead" = "1")
mydata$Outcome2 <- as.numeric(mydata$Outcome2)
{r Kaplan-Meier}
mydata %>%
finalfit::surv_plot(dependent,
explanatory,
xlab='Time (months)',
pval=TRUE,
legend = 'none',
break.time.by = 12,
xlim = c(0,60),
legend.labs = c('a','b')
)
{r}
km_fit <- survfit(dependent ~ explanatory,
data = mydata)
km_fit
{r, eval=FALSE, include=FALSE}
library(survival)
km <- with(mydata, Surv(OverallTime, Outcome2))
# head(km,80)
# plot(km)
{r 1-3-5-yr}
summary(km_fit, times = c(12,36,60))
{r}
survminer::pairwise_survdiff(formula = Surv(time, Outcome) ~ Group,
data = mydata,
p.adjust.method = "BH")
{r Multivariate Analysis, eval=FALSE, include=FALSE}
library(finalfit)
library(survival)
explanatoryMultivariate <- explanatoryKM
dependentMultivariate <- dependentKM
mydata %>%
finalfit(dependentMultivariate, explanatoryMultivariate) -> tMultivariate
knitr::kable(tMultivariate, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r"))
Docker Containers for the R Environment
docker run --rm -ti rocker/r-base
Or get started with an RStudio® instance:
docker run -e PASSWORD=yourpassword --rm -p 8787:8787 rocker/rstudio
and point your browser to localhost:8787 Log in with user/password rstudio/yourpassword
{r load library}
source(file = here::here("R", "loadLibrary.R"))
{r}
saved data after analysis to `mydata.xlsx`.
save.image(file = here::here("data", "mydata_work_space.RData"))
readr::write_rds(x = mydata, path = here::here("data", "mydata_afteranalysis.rds"))
saveRDS(object = mydata, file = here::here("data", "mydata.rds"))
writexl::write_xlsx(mydata, here::here("data", "mydata.xlsx"))
paste0(rownames(file.info(here::here("data", "mydata.xlsx"))), " : ", file.info(here::here("data", "mydata.xlsx"))$ctime)
{r}
citation()
To cite R in publications use:
R Core Team (2019). R: A language and environment for
statistical computing. R Foundation for Statistical Computing,
Vienna, Austria. URL https://www.R-project.org/.
A BibTeX entry for LaTeX users is
@Manual{,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2019},
url = {https://www.R-project.org/},
}
We have invested a lot of time and effort in creating R, please
cite it when using it for data analysis. See also
'citation("pkgname")' for citing R packages.
{r library citations}
citation("tidyverse")
citation("readxl")
citation("janitor")
citation("report")
citation("finalfit")
citation("ggstatplot")
The jamovi project (2019). jamovi. (Version 0.9) [Computer Software]. Retrieved from https://www.jamovi.org.
R Core Team (2018). R: A Language and envionment for statistical computing. [Computer software]. Retrieved from https://cran.r-project.org/.
Fox, J., & Weisberg, S. (2018). car: Companion to Applied Regression. [R package]. Retrieved from https://cran.r-project.org/package=car.
{r session info, echo=TRUE}
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.6.0 magrittr_1.5 formatR_1.7 htmltools_0.4.0
[5] tools_3.6.0 yaml_2.2.0 Rcpp_1.0.2 codetools_0.2-16
[9] stringi_1.4.3 rmarkdown_1.16 rmdshower_2.1.1 knitr_1.25
[13] stringr_1.4.0 xfun_0.10 digest_0.6.21 rlang_0.4.1
[17] evaluate_0.14
https://sbalci.github.io/MyRCodesForDataAnalysis/R-Markdown.nb.html https://sbalci.github.io/MyRCodesForDataAnalysis/R-Markdown.html
Completed on 2019-11-01.
Serdar Balci, MD, Pathologist
drserdarbalci@gmail.com
https://rpubs.com/sbalci/CV
https://sbalci.github.io/
https://github.com/sbalci
https://twitter.com/serdarbalci